Journal Metrics

Source Normalized Impact per Paper (SNIP): 2.252ℹSource Normalized Impact per Paper (SNIP):2014: 2.252SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.

SCImago Journal Rank (SJR): 0.848ℹ

SCImago Journal Rank (SJR):2014: 0.848SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.

Stay up-to-date

Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.

The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.

This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.

The Journal of Computational Science aims to be an international platform to exchange novel research results in simulation based science across all scientific disciplines. It publishes advanced innovative, interdisciplinary research where complex multi-scale, multi-domain problems in science and engineering are solved, integrating sophisticated numerical methods, computation, data, networks, and novel devices.

The journal welcomes original, unpublished high quality contributions in the field of computational science at large, addressing one or more of the aforementioned elements.

Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.

The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.

This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.